Data analysis and data classifications systems may often generate and store metadata that indicates various properties and/or characteristics of data items (e.g., files, messages, documents, etc.). This metadata, which may be appended to or stored with data items, may enable users to search and/or organize large sets of data. For example, a search engine may identify files relevant to a user's search based on stored attributes of archived files. In addition, classifying or categorizing data items may enable a service (e.g., a messaging service or a security service) to appropriately handle and/or store the data items. As an example, an email client may sort received messages into appropriate folders or containers based on properties of the messages.
While traditional methods for classifying and storing data items may provide useful information about the characteristics of the data items, these methods may fail to provide a comprehensive history or evaluation of the metrics or rules by which data items are analyzed. Even if a conventional data classification system is capable of tracking changes in classification rules (e.g., by auditing or restoring previous rule sets), this process may be expensive and/or time-consuming. As such, valuable information regarding changes in both data items and classification rules may be lost. The instant disclosure, therefore, identifies and addresses a need for systems and methods for evaluating and storing data items.